



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 				_a				(   _lowerCAmelCase					):
			A      		=	(EulerDiscreteScheduler,)
			A      		=	10

			def      __snake_case       (self,   **SCREAMING_SNAKE_CASE_					)       ->   Tuple:
						UpperCAmelCase_:		List[str]							      =     {
						    """num_train_timesteps""": 1100,
						    """beta_start""": 0.0_0_0_1,
						    """beta_end""": 0.0_2,
						    """beta_schedule""": """linear""",
						}

						config.update(**SCREAMING_SNAKE_CASE_					)
						return config

			def      __snake_case       (self					)       ->   Union[str, Any]:
						for timesteps in [10, 50, 100, 1000]:
									self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Dict:
						for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1],   [0.0_0_0_2, 0.0_0_2, 0.0_2]					):
									self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_,   beta_end=SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   List[Any]:
						for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   List[str]:
						for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_:		Tuple							      =     self.scheduler_classes[0]
						UpperCAmelCase_:		Union[str, Any]							      =     self.get_scheduler_config()
						UpperCAmelCase_:		Optional[int]							      =     scheduler_class(**SCREAMING_SNAKE_CASE_					)

						scheduler.set_timesteps(self.num_inference_steps					)

						UpperCAmelCase_:		Tuple							      =     torch.manual_seed(0					)

						UpperCAmelCase_:		Any							      =     self.dummy_model()
						UpperCAmelCase_:		Dict							      =     self.dummy_sample_deter * scheduler.init_noise_sigma
						UpperCAmelCase_:		int							      =     sample.to(SCREAMING_SNAKE_CASE_					)

						for i, t in enumerate(scheduler.timesteps					):
									UpperCAmelCase_:		Union[str, Any]							      =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		Union[str, Any]							      =     model(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		Dict							      =     scheduler.step(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   generator=SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Optional[Any]							      =     output.prev_sample

						UpperCAmelCase_:		str							      =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_					)					)
						UpperCAmelCase_:		List[Any]							      =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_					)					)

						assert abs(result_sum.item() - 1_0.0_8_0_7					) < 1E-2
						assert abs(result_mean.item() - 0.0_1_3_1					) < 1E-3

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Tuple							      =     self.scheduler_classes[0]
						UpperCAmelCase_:		Optional[int]							      =     self.get_scheduler_config(prediction_type="""v_prediction"""					)
						UpperCAmelCase_:		List[Any]							      =     scheduler_class(**SCREAMING_SNAKE_CASE_					)

						scheduler.set_timesteps(self.num_inference_steps					)

						UpperCAmelCase_:		Union[str, Any]							      =     torch.manual_seed(0					)

						UpperCAmelCase_:		str							      =     self.dummy_model()
						UpperCAmelCase_:		Any							      =     self.dummy_sample_deter * scheduler.init_noise_sigma
						UpperCAmelCase_:		List[Any]							      =     sample.to(SCREAMING_SNAKE_CASE_					)

						for i, t in enumerate(scheduler.timesteps					):
									UpperCAmelCase_:		Optional[Any]							      =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		Optional[Any]							      =     model(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		str							      =     scheduler.step(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   generator=SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Any							      =     output.prev_sample

						UpperCAmelCase_:		Tuple							      =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_					)					)
						UpperCAmelCase_:		int							      =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_					)					)

						assert abs(result_sum.item() - 0.0_0_0_2					) < 1E-2
						assert abs(result_mean.item() - 2.2676E-06					) < 1E-3

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Dict							      =     self.scheduler_classes[0]
						UpperCAmelCase_:		List[Any]							      =     self.get_scheduler_config()
						UpperCAmelCase_:		Union[str, Any]							      =     scheduler_class(**SCREAMING_SNAKE_CASE_					)

						scheduler.set_timesteps(self.num_inference_steps,   device=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		int							      =     torch.manual_seed(0					)

						UpperCAmelCase_:		Optional[Any]							      =     self.dummy_model()
						UpperCAmelCase_:		Tuple							      =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						UpperCAmelCase_:		Optional[Any]							      =     sample.to(SCREAMING_SNAKE_CASE_					)

						for t in scheduler.timesteps:
									UpperCAmelCase_:		Any							      =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		List[str]							      =     model(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		int							      =     scheduler.step(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   generator=SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Dict							      =     output.prev_sample

						UpperCAmelCase_:		List[str]							      =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_					)					)
						UpperCAmelCase_:		List[Any]							      =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_					)					)

						assert abs(result_sum.item() - 1_0.0_8_0_7					) < 1E-2
						assert abs(result_mean.item() - 0.0_1_3_1					) < 1E-3

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_:		List[Any]							      =     self.scheduler_classes[0]
						UpperCAmelCase_:		int							      =     self.get_scheduler_config()
						UpperCAmelCase_:		Union[str, Any]							      =     scheduler_class(**SCREAMING_SNAKE_CASE_,   use_karras_sigmas=SCREAMING_SNAKE_CASE_					)

						scheduler.set_timesteps(self.num_inference_steps,   device=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		int							      =     torch.manual_seed(0					)

						UpperCAmelCase_:		int							      =     self.dummy_model()
						UpperCAmelCase_:		Union[str, Any]							      =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						UpperCAmelCase_:		Tuple							      =     sample.to(SCREAMING_SNAKE_CASE_					)

						for t in scheduler.timesteps:
									UpperCAmelCase_:		Any							      =     scheduler.scale_model_input(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		Optional[Any]							      =     model(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

									UpperCAmelCase_:		List[str]							      =     scheduler.step(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   generator=SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		Dict							      =     output.prev_sample

						UpperCAmelCase_:		Optional[int]							      =     torch.sum(torch.abs(SCREAMING_SNAKE_CASE_					)					)
						UpperCAmelCase_:		List[str]							      =     torch.mean(torch.abs(SCREAMING_SNAKE_CASE_					)					)

						assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9					) < 1E-2
						assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3					) < 1E-3











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a      :  Dict			     =			{
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Any			     =			['LxmertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			[
	    'LxmertEncoder',
	    'LxmertForPreTraining',
	    'LxmertForQuestionAnswering',
	    'LxmertModel',
	    'LxmertPreTrainedModel',
	    'LxmertVisualFeatureEncoder',
	    'LxmertXLayer',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			[
	    'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFLxmertForPreTraining',
	    'TFLxmertMainLayer',
	    'TFLxmertModel',
	    'TFLxmertPreTrainedModel',
	    'TFLxmertVisualFeatureEncoder',
	]


if TYPE_CHECKING:
	from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
	from .tokenization_lxmert import LxmertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_lxmert_fast import LxmertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lxmert import (
		    LxmertEncoder,
		    LxmertForPreTraining,
		    LxmertForQuestionAnswering,
		    LxmertModel,
		    LxmertPreTrainedModel,
		    LxmertVisualFeatureEncoder,
		    LxmertXLayer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_lxmert import (
		    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLxmertForPreTraining,
		    TFLxmertMainLayer,
		    TFLxmertModel,
		    TFLxmertPreTrainedModel,
		    TFLxmertVisualFeatureEncoder,
		)

else:
	import sys

	a      :  str			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






